SOTAVerified

Object Tracking

Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.

( Image credit: Towards-Realtime-MOT )

Papers

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Showing 1851–1900 of 1966 papers

TitleStatusHype
MONCE Tracking Metrics: a comprehensive quantitative performance evaluation methodology for object tracking—0
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering—0
MonoNext: A 3D Monocular Object Detection with ConvNext—0
MOPT: Multi-Object Panoptic Tracking—0
MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection—0
MOT20: A benchmark for multi object tracking in crowded scenes—0
MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking—0
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric—0
MOT FCG++: Enhanced Representation of Spatio-temporal Motion and Appearance Features—0
Motion-guided small MAV detection in complex and non-planar scenes—0
Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision—0
Motion Prediction in Visual Object Tracking—0
Motion Prediction on Self-driving Cars: A Review—0
MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion—0
MotionTrack: Learning Motion Predictor for Multiple Object Tracking—0
MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking—0
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking—0
MOTSLAM: MOT-assisted monocular dynamic SLAM using single-view depth estimation—0
MOTS: Multi-Object Tracking and Segmentation—0
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method—0
MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking—0
Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective—0
DecoderTracker: Decoder-Only Method for Multiple-Object Tracking—0
MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark—0
MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos—0
MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark—0
Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking—0
Multi-Branch Siamese Networks with Online Selection for Object Tracking—0
Multi-camera Multi-Object Tracking—0
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles—0
Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using Hidden Markovian Chains—0
Multi-Class Multi-Object Tracking using Changing Point Detection—0
Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking—0
Multi-Forest Tracker: A Chameleon in Tracking—0
Multi-modal Tracking for Object based SLAM—0
Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps—0
Multi-Object Tracking and Identification over Sets—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multi-Object Tracking as Attention Mechanism—0
Multi-Object Tracking based on Imaging Radar 3D Object Detection—0
Multi-object Tracking by Detection and Query: an efficient end-to-end manner—0
Multi-Object Tracking by Hierarchical Visual Representations—0
Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring—0
Multi-Object Tracking for Collision Avoidance Using Multiple Cameras in Open RAN Networks—0
Multi-Object Tracking Meets Moving UAV—0
Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution—0
Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles—0
Multi-Object Tracking via Constrained Sequential Labeling—0
Multi-object Tracking via End-to-end Tracklet Searching and Ranking—0
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All datasetsCOESOTFE108QuadTrackSeaDronesSeeBIRDSAI - ICVGIP 2020KITTIMMPTRACKPerception TestVisEvent

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HR-CEUTrack-LargeSuccess Rate65—Unverified
2HR-CEUTrack-BaseSuccess Rate63.2—Unverified
3CEUTrack-LargeSuccess Rate62.8—Unverified
4CEUTrack-BaseSuccess Rate62—Unverified
5SiamR-CNNSuccess Rate60.9—Unverified
6TransTSuccess Rate60.5—Unverified
7SuperDiMPSuccess Rate60.2—Unverified
8TrDiMPSuccess Rate60.1—Unverified
9KeepTrackSuccess Rate59.6—Unverified
10AiATrackSuccess Rate59—Unverified
#ModelMetricClaimedVerifiedStatus
1HR-MonTrack-BaseSuccess Rate68.5—Unverified
2HR-MonTrack-TinySuccess Rate66.3—Unverified
3Multi-modalSuccess Rate63.4—Unverified
4PrDiMPSuccess Rate59—Unverified
5DiMPSuccess Rate57.1—Unverified
6MonTrackSuccess Rate54.9—Unverified
7ATOMSuccess Rate46.5—Unverified
8KYSSuccess Rate26.6—Unverified
#ModelMetricClaimedVerifiedStatus
1OmniTrackHOTA23.45—Unverified
2DeepSORTHOTA21.16—Unverified
3OC-SORTHOTA20.83—Unverified
4ByteTrackHOTA20.66—Unverified
5TrackFormerHOTA19.62—Unverified
6HybridSORTHOTA16.64—Unverified
7DiffMOTHOTA16.4—Unverified
8Bot-SORTHOTA15.77—Unverified
#ModelMetricClaimedVerifiedStatus
1DiMP50Success Rate67.33—Unverified
2PrDiMP50Success Rate67—Unverified
3PrDiMP18Success Rate65.9—Unverified
4DiMP18Success Rate64.6—Unverified
5AtomSuccess Rate63.8—Unverified
#ModelMetricClaimedVerifiedStatus
1finalHumans0.14—Unverified
2night_furyHumans0.05—Unverified
3Yolo based methodHumans0.02—Unverified
4finalHumans0—Unverified
#ModelMetricClaimedVerifiedStatus
1M2-Trackmean precision83.4—Unverified
2BATmean precision75.2—Unverified
#ModelMetricClaimedVerifiedStatus
1UMMT3DMOTA95—Unverified
2MMPTRACK3DMOTA94.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Siam-FCAverage IOU0.66—Unverified
#ModelMetricClaimedVerifiedStatus
1RT-MDNetPrecision Plot0.63—Unverified